Congestion pricing is a win-win strategy and the only way to truly reduce traffic congestion

The urban transportation problem is a hardy perennial: no matter how many lanes we add to urban freeways, traffic congestion is just as bad, if not worse than ever. In the face of “free” road travel, induced demand means that supply side strategies like widening freeways simply encourage more people to drive, and congestion gets worse. Houston successively widened its Katy Freeway to 23 lanes, but only found that congestion got worse and travel times got longer.

Fortunately there is a solution: peak-hour road pricing. It’s been successfully implemented in cities around the world, from London, to Singapore, to Milan to Stockholm. Charging a modest price for the use of roads at the peak hour encourages a small fraction of drivers to change their route or mode, with the result that traffic levels and travel times improve for remaining users. And while there’s always skepticism and resistance to charging for something that everyone pretended was free, once these systems are put in place, they have broad popular support because they actually make travel better and easier. Stockholm’s system was initially opposed by the public, but after six months of operation, was approved in a popular referendum by 51 percent; today more than 70 percent of Stockholm residents support the system.

The economics of this problem are simple and straightforward: as long as we charge a zero price for the use of scarce and valuable peak hour road capacity, more people will use it than it can handle. Ultimately, we end up rationing road space by the degree of road user’s desperation and their tolerance for delays. As we’ve suggested at City Observatory, it’s as if we’re trying to run the urban road system the way Ben & Jerry’s runs their ice cream business the one day of the year that they give cones away for free, and with similar results: People line up around the block for “free” ice cream, which is actually only available to those with the patience to wait long periods in line. There’s no way for Ben and Jerry’s to build enough ice cream stores to meet the demand for free cones–and free ice cream everyday would soon bankrupt the company.

This is one instance where charging a price for a “free” good will make things better for everyone. Here are two important things to keep in mind. First, not everyone attaches the same value to a peak hour roadway trip. Second, traffic is non-linear.  Here’s why these things matter.

One working assumption seems to underly popular discussions about peak hour travel: It’s that somehow everyone who is using the road now “needs” to be on the road at that time. But in fact, a big fraction of travelers have options. They could travel at  a different time (a little earlier, or a little later), they could take a different route or mode (like bus or bike), or they could combine or re-route trips. Some people would find it just as convenient to travel at a different time or to a different destination, and while others might forego a trip altogether.  Perhaps a majority of those currently traveling at the peak hour would find it difficult or inconvenient to change, but many people could be flexible, if they had the incentive to do so. They key question is how many people would have to change their behavior to get a positive result.

That’s the second key fact: The answer is that only a few people would need to forego current peak hour car trips to get a big improvement in congestion. A critical feature of traffic congestion is that it’s non-linear.  What that means in practice is that roads work really, really well, right up to the point where they get congested. A highway with heavy traffic moving along at 45 to 50 miles per hour carries the most vehicles of any roadway. But add just a few more cars, and the traffic level reaches a tipping point, where things slow down, and the road actually loses its ability to move people. The key to making roads work well is to keep them just below this critical tipping point. The good news here is reducing traffic volumes just 5 to 10 percent is all that is needed in many instances to keep the roads moving.

We’re not talking about big charges either:  in most cases, modest toll levels trigger big changes in commuting patterns. Louisville, Kentucky recently started tolling the I-65 crossing over the Ohio River. Regular commuters pay a toll of $1 per crossing; and tolls have reduced the volume of cars using the I-65 crossing by more than 40 percent. Today, you encounter almost zero traffic at rush hour leaving downtown Louisville.  (As it turns out, Louisville wasted roughly a billion dollars widening this bridge, because they doubled it’s capacity before levying a toll; if they’d tolled first, they would have discovered they had plenty of road space).

So to make our roads work better, all we need are tolls that persuade maybe one in ten travelers to change their behavior. And that’s what peak hour pricing does: sends a signal to those people who have alternatives or who least value traveling at that time on that route, to do something different. The result is that every other traveler gets a big benefit in terms of improved performance.

A well-designed system of road pricing could be an enormous win-win:  It would shorten travel times, and actually improve the carrying capacity of the highway system. It would eliminate the need for expensive, environmentally destructive and usually wasteful road-widening projects. The funds from road pricing could be used–as they are in London–to fund other transportation alternatives. There’s really no such thing as a “freeway,” and a correctly priced road system would be better for all of us.

 

What to make of the high credit scores of new renters in some markets: alarm bell or success signal?

RentCafe–one arm of Yardi Matrix, a real estate data and services firm–has a very interesting new data series on the credit scores of successful and unsuccessful apartment seekers in different cities.  Rent Cafe runs a tenant screening service on behalf of landlords, and a key variable that they report to their clients is a household’s credit score (which ranges from a low of 300 to a high of 800).  Rent Cafe’s research has shown a strong correlation between a strong credit score and a high probability of having your rental application approved.  Nationally, if you have a credit score over 750, your chances are 98%, if it’s between 500 and 600, then you have only about an 67% chance of getting the apartment.

In some markets–like Boston, San Francisco and Seattle–successful rental applicants have sterling credit. In these top cities, new renters have credit scores over 700.

To Rent Cafe, the high scores in these cities are a kind of alarm bell. You apparently need really great credit to rent an apartment in these places.

What Credit Score Do You Need to Rent an Apartment? Insanely High, If You’re in Boston or San Francisco

But there’s another possible view: it’s likely that high credit scores signal just how robust the local economies are in these communities, and furthermore, how much households with strong credit are choosing to rent rather than to buy.  Given that most of these communities have very expensive home prices, it may be that many credit-worthy households who might otherwise prefer to buy a home end up renting.  And because Rent Cafe is reporting the average credit score, the average renter in these cities just has better credit.

Conversely, in some other cities, if you have a high credit score, you may find it more affordable to purchase a home rather than renting. This selection effect would explain why renters have much higher average credit scores in high-priced markets than in low-priced markets.

It’s also likely that credit scores are rising because the personal financial situation of the typical renter is getting better.  Rent Cafe notes that the credit score of the typical approved applicant has increased about 12 points in the past three years, from 638 in 2014 to 650 this year. This is consistent with other evidence showing steady income gains in most metro economies. We’ve reported on national data at City Observatory. Oregon economist Josh Lehner has a series showing income gains for the Portland area (which ranks 8th in Rent Cafe’s list of metros with the highest credit scores for renters). Rising incomes are making housing more affordable.

 

Clearly, the rental market is very competitive in places like Boston and San Francisco, meaning landlords can pick and choose among tenants with a strong financial background; but the very strong credit scores of successful rental applicants in these cities  is as much an indication of strong local economies  (and prosperous tenants) as it is of the choosiness of local landlords. In the long run, the growing average creditworthiness of tenants may reflect the limited opportunities for homeownership in these increasingly expensive markets, where households with sterling credit who would be buyers almost anywhere else, find themselves renting instead. A new report issued by the Philadelphia Federal Reserve Bank shows a continued decline in the number of first-time home buyers nationally, a trend consistent with rising average credit scores among renters.

You’l want to take a look at the full report. The RentCafe report has detailed information on average credit scores of successful and unsuccessful apartment seekers in 100 of the nation’s largest cities. As we’ve noted, the data are drawn from RentCafe’s own client work, and so may be influenced by how complete and representative a sample of properties it covers in each market.

Will two firms produce enough effective competition to benefit consumers?

The use ride-hailing services continues to grow in the US, and while there are a range competitors in some markets, like New York, in most places, nearly all ride-hailing is dominated by Uber and Lyft. The good news for consumers is that fierce competition for market share between these firms is helping to keep fares low, and promote steady innovation and change.

While our view has long been that we want to encourage a range competitors–”Let a thousand Ubers Bloom,” we said–that doesn’t seem to be happening yet. Austin, which was temporarily evacuated by both companies after a local voter-approved measure which enacted some restrictions (such as background checks for drivers), seemed to be a keen example of how new entrants could step in and fill the void. But this summer, after the Texas Legislature pre-empted Austin’s municipal laws, Uber and Lyft re-entered the market with a vengeance, and have apparently recovered most of their lost market share. Local startups are struggling against the bigger firms brand recognition and and deep pockets. One local startup, RideAustin, saw its business decline 62 percent after Uber and Lyft came back to town.

In the longer term, the burgeoning number of businesses developing autonomous vehicles are potential competitors for Lyft and Uber. Waymo, the Google/Alphabet subsidiary, has just launched its fully driverless cars in Phoenix, and as they scale up, they threaten to be a major disruptor in this market. But that development seems to still be a ways off.  Will two firms in this industry be enough to assure vigorous price competition, and provide consumers with good value? Let’s look to see what’s happened here in the past few months.

Will a duopoly be enough?

Uber has been the dominant firm in the ride-hailing market since its inception, but in recent months there are signs that its dominance is ebbing. The company has experienced a series of widely publicized gaffes, ranging from sexual harassment claims against its executives, to a video of the company CEO disparaging one of its drivers, to the ultimate resignation of founder Travis Kalanick. The bad publicity, coupled with a #deleteUber campaign has had an impact. During most of the past calendar year, rival ride-hailing firm Lyft has grown faster, and picked up market share. Nationally, estimates are that Uber’s market share has fallen from more than 80 percent to less than 75 percent.

But the national statistics obscure a fiercer battle in many cities. Instead of being a weak also-ran, Lyft appears to becoming a sizable competitor in many markets.  Data compiled by credit card analytics firm Second Measure estimates the market share of the two companies in different cities around the country. As reported in Recode, these data show strong improvements in Lyft’s position in many markets. Across the dozen markets shown here, Lyft has recorded 10 point or better gains in share in three-quarters of them. In general, Lyft has made the biggest inroads in major West Coast markets; in Portland, Lyft has a 45 percent market share, making it a very close rival to Uber.

A companion article in Recode examines the connection between the #deleteUber campaign and consumer use of the two services.  According to Second Measure’s data, most ride hailing customers exclusively use just one of the two leading services (71 percent exclusively use Uber, 19 percent exclusively use Lyft).  Only about 10 percent of ride-hailing customers use both. While customer loyalty to a single firm is something that every company no doubt wants to cultivate, the sharp change in the market share of the two companies is good evidence that switching costs are relatively low. Thus, even though only a small fraction of customers are regular comparison shoppers (using both services routinely), those who rely primarily on one or the other can easily switch if they’re dissatisfied. Low switching costs are one of the keys to getting the benefits of competition in a duopoly situation.

An intensely competitive duopoly in ride-hailing is likely to help assure better rates and better services for consumers than would be the case if one company achieved monopoly status. If customers are dissatisfied over prices, the service the receive or the company’s policies, they can vent their displeasure by switching to the alternative. This market share shift however suggests that the market valuation attached to Uber–almost more than $70 billion last year–may be far too high. While we may not see a thousand flowers blooming, an effective duopoly may offer the next best thing.

Slowly, the rhetorical battle is being won, as affordable housing advocates acknowledge more supply matters

There’s been a war of words about what kind of housing policies are needed to address the nation’s affordability problems. Economists (from the White House to academe) argue that increasing housing supply is essential. Low income housing advocates of many stripes push for efforts to pay via taxes or require via regulations that more units be built specifically for low and moderate income households.

Everyone agrees that much of the affordability problem is due to national policies that provide massive subsidies to homeownership by the wealthy (mostly through the tax code) and parsimonious and chronically underfunded programs that provide subsidies for the poor (which reach less than a quarter of those technically eligible), a fair share of responsibility lies in the hand of local governments.  Is the key relaxing zoning limits to allow more market rate housing, or does it require regulating or subsidizing more affordable units into existence.

The battle cry of the low income housing advocates is “you can’t build your way to affordability.” As Bloomberg’s Noah Smith put it, among these advocates:

 . .  it has become an article of faith that building market-rate housing raises rents, rather than lowers them.  The logic of Econ 101 — that an increase in supply lowers price — is alien to many progressives, both in the Bay Area and around the country.

Sightline Institute has tackled that notion directly. Not only can  you build your way to affordable housing, in fact, building more supply may be the only effective way to reduce the pressure that is driving up rents and producing displacement. There’s ample evidence for this position, but there’s still the strong sense that addressing our housing problem by building more high end housing is a cynical and ineffective kind of “trickle down” economics.”

Building more market rate housing isn’t so much about “trickle down” as it is building enough new housing to keep higher income households from moving down-market and bidding up the price of older housing that would otherwise be affordable to moderate and lower income households. When there isn’t enough supply, demand from higher income households floods down to older housing stock, driving up rents and reducing housing options for those with lesser means. Which, as why, as we’ve observed, in some markets, modest 1950’s-era ranch homes are a mainstay of affordability, while in others, they cost more than a million bucks.

When supply does catch up to demand, rents tend to fall across the market. Last month, we showed how the completion of thousands of new, market rate apartments in Portland was having the knock-on effect of creating a growing number of vacancies and a flood of “FOR RENT” signs in the city’s older apartment stock. Rent increases, which were measured in the double digits eighteen months ago, have gone negative.

As Sightline Institute’s clever musical chairs metaphor makes clear, it doesn’t matter whether you add fancy overstuffed arm-chairs or or simple folding metal chairs to the game, both make it equally likely at the end of the day that there will be a closer match between chairs and hind-ends than otherwise.

What about “filtering up?”

The latest salvo in the rhetorical battles over the merits of expanding market rate housing supply comes from Miriam Axel-Lute, writing for Shelterforce.  Her latest article “Trickle Up Housing: Filtering does go both ways” makes the case that building new market rate housing does somewhat ameliorate displacement and affordability issues, that building more new low and moderate income is a more direct and powerful solution. The argument here is that if we build more housing for the poorest among us, that will free up some units for other perhaps slightly less poor households.

She buttresses the case for “trickle-up” housing by citing a study from U. C. Berkeley’s Karen Chapple and Miriam Zuk, that claims that building affordable units is twice as effective in reducing displacement as building more market rate housing. The exact claim, quoted from Chapple and Zuk is:

“At the regional level, both market-rate and subsidized housing reduce displacement pressures, but subsidized housing has over double the impact of market-rate units”

“Double the impact” sounds more like a pitch for a new and improved laundry detergent than a calculated analysis of housing policy options, but it did pique our curiosity.  How did Chapple and Zuk determine the relative effectiveness of these two policies?

As it turns out, their work is a response to a widely cited analysis developed by the California Legislative Analyst’s Office (LAO), which looked at the connection between displacement and housing construction in the Golden State.  The LAO’s conclusion was that building more market rate housing reduced displacement. Chapple and Zuk questioned the LAO report for omitting the possible positive effects of building more subsidized housing. They ran a regression analysis looking at both market rate and subsidized housing and controlling for the impact of a number of other factors, including age of the housing stock, racial/ethnic composition of the population, and education. They find that building more market rate units decreases measured displacement, as they put it:

Consistent with the LAO Report, we find that new market-rate units built from 2000 to 2013 significantly predict a reduction in the displacement indicator . . .

They also find that the construction of more subsidized housing also results in a reduction of the displacement indicator.  The claim of “twice the impact” comes from comparing the coefficients of the two variables, the coefficient on market rate housing is -.002; the coefficient for subsidized housing is -.005.  So it is literally the case that the coefficient of one is more than twice as large as the other.

Two market rate houses reduce displacement as much as one affordable house

But let’s step back and consider what that means in practice. What is measured in each case is the number of new market rate homes and the number of new subsidized homes built in a community over a decade. Put another way, the construction of one, new market rate home has almost half as much impact on measured displacement as building a subsidized home. What this means in practice is that two or three new $600,000 single family homes or condominiums built in the Bay Area in the last decade or so reduced displacement in the region by as much as building a new subsidized unit. On its face, this study puts to rest the old saw that building more market rate housing leads to more displacement: it doesn’t. In fact, building more market rate housing is an effective anti-displacement strategy.

In addition to effectiveness, we also have to consider cost. If government’s faced a costless choice between so many market rate units and and equal number of subsidized units, and the only policy objective were reducing displacement, the answer would be clear. But building subsidizing housing is hugely expensive for the public sector. As we pointed out last month, in the Bay Area, new subsidized housing in San Francisco, and in the East Bay costs as much as $700,000 per unit.

There’s a further point to be made here:  one of the key reasons that “affordable” housing is so expensive to build in places like San Francisco is that land prices are very high because of zoning restrictions. As a result, the same policies that facilitate market rate housing–more density, fewer parking requirements, clear and certain approval processes–would also make it less expensive to build affordable housing.

So it is literally true that building more subsidized units for the lower income households is more powerful in reducing displacement. But its tremendously expensive as well. What the data here confirm, is what economists–and musical chairs aficionados–have long maintained: increasing supply is critical to solving the housing affordability problem.

 

We review Yelp’s new index for rating local economies:  It’s a good start

For a long time, the only comprehensive and reliable means we’ve had of tracking and comparing economic activity across state and regional economies has been official government statistics, such as those compiled by the Census Bureau and the Bureau of Labor Statistics. While these data have many virtues, there are often significant lags between the time data is collected and the time it is reported, especially for data with high levels of geographic or industry detail. In addition, as a rule, government data strictly protect the confidentiality of individuals, and so suppress data that might the identity or precise location of a particular household or business.

The advent of big data, in the form of massive databases augmented with crowd-sourced information, adds a new dimension to our ability to track and measure local economies. One of the most exciting sources is Yelp, which tracks and publishes user reviews of millions of businesses. Yelp has just introduced its new “local economic outlook” which rates cities and neighborhoods based on their “economic opportunity.” The rankings are based on Yelp’s extensive data, and are summarized in the form of national rankings of cities (and a parallel rankings of the top 50 neighborhoods).

Yelp has done its homework. Ed Glaeser, the dean of the nation’s regional economists, has authored a paper with two other economists testing the validity of Yelp’s business count data against the Census Bureau’s County Business Patterns data (CBP).  CBP is generally only available with a lag of a year or more and so isn’t a good guide to what’s happening right now in regional economies.  Glaeser and his co-authors find that Yelp’s data does a good job of predicting future trends in CBP data.  There is some variability: Yelp’s counts tend to be most accurate in dense, well-educated and higher income areas.  Also–unsurprisingly–Yelp’s coverage and accuracy has been steadily improving over time, and more closely agrees now with official statistics than it did just a few years ago.

It’s fantastic to get this data, but at least in its first iteration Yelp provides only rankings and doesn’t publish specific numerical estimates. We don’t know, for example, whether the cities in the top ten are 1 percent, 10 percent or 50 percent better than the median. All we have, in effect, is a top-to-bottom ranking of cities according to Yelp’s economic opportunity index. As we’ve always maintained, simple rankings that omit scalar data tend to generate a lot of heat, but actually shed little light. The Yelp report would be much more useful and interesting if it reported the actual numerical values of their index, and updated these figures on a monthly or quarterly basis. Yelp’s Carl Bialik tells us via email that they are working to extend the opportunity scores for future quarters, including releasing more raw data. We’re looking forward to this.

Because it is different from most conventional measures of economic activity, and because it is so new, it’s still a bit difficult to know exactly what the opportunity index measures, and whether its survival probabilities reflect short-term or more enduring differences in economic climate. It’s apparent from the methodological explanation (below) that they are looking at the opening and closing of business establishments, and using this information to compute survival probabilities in particular areas. But since they don’t publish numerical values for individual cities, or precisely reveal the formula for computing the index, its hard to interpret the rankings, or see how the opportunity index squares with other widely used measures of local economic activity. As we see more of the detail from the index, and can calibrate it against other measures, we’ll get a better idea of what the index is signaling and what it means.

The Yelp data is a promising and tantalizing look at how big, crowd-sourced data can help us develop a more timely and nuanced understanding of local economic activity. While their current rankings are a good way to promote awareness of the data, we hope they’ll do even more in the months ahead to publish and regularly update specific metro market indicators that others can use.